Henning Hermjakob

The paper presents a manually curated dataset of protein binding domains, linking them to molecular interactions and genomic variations for benchmarking and disease mechanism studies.

DOME Copilot automates the generation of transparent AI methodology reports for life science publications, matching or exceeding manual quality while reducing creation time.

This paper applies large language models to assist in gene annotation into Reactome pathways, showing promising results in supporting curation workflows.

RNAcentral integrates literature and gene-level data to enhance non-coding RNA sequence analysis and functional understanding.

The paper updates and corrects SED-ML files for 1055 models in BioModels to improve reproducibility and verification across simulation engines.

Talk2Biomodels provides an AI platform for interacting with biological models using natural language, enhancing accessibility and FAIR principles.

Machine learning integration of over 25,000 mass spectrometry experiments yields 15,326 human protein complexes covering 13,769 proteins, plus 511 AlphaFold-derived mutually exclusive subunit pairs.

The paper proposes adapting FAIR indicators to assess and improve the quality and transparency of computational models in biology.

The paper defines AI-readiness criteria for biomedical data, encompassing seven dimensions, and evaluates them across flagship datasets to ensure ethical and explainable AI practices.

The paper introduces FROG analysis to standardize reproducibility assessments of genome-scale metabolic models, resulting in full reproducibility of all tested models.

Poincare and SimBio are Python packages for CRN modeling and simulation, offering extensibility and performance through Numba compilation and SBML support.

The paper constructs a tissue-specific protein interaction atlas using proteomic data from 11 human tissues, identifying key drivers of inter-tissue variation and prioritizing disease genes in brain disorders.

The paper annotates disease variants in the context of biological pathways to infer molecular phenotypes and model variant impacts.

This paper explores using ChatGPT in Reactome curation to automate pathway annotation, comparing its outputs to manual curation and identifying challenges and improvements.

The paper analyzes microRNA interactions in three rare disorders to assess data availability and annotation consistency across databases.

The paper predicts functional interactions of dark proteins with Reactome pathways using a random forest classifier and develops a web portal to visualize these interactions.

This paper introduces a protocol to create a FAIR collection of machine learning models in life sciences and medicine, aiming to improve reproducibility and reuse through community curation.

The paper uses network-based expansion of GWAS results to identify pleiotropic gene modules linked to multiple traits and demonstrates their application in studying inflammatory bowel disease.

The paper introduces a decoupled, modular curation architecture for data platforms, enabling flexible and scriptable workflows without modifying existing infrastructure.

The paper curates a Leishmania infection pathway in Reactome to enhance pathway analysis of transcriptomic data from macrophage infections, revealing new insights into IL10 and IL6 modulation.

The paper investigates reproducibility of systems biology models, finding that 49% of 455 models could not be reproduced due to missing data or inconsistencies.

The paper curates and expands the yeast complexome, showing increased genetic interactions and functional links among complex members.

ReactomeGSA enables multi-omics, cross-species pathway analysis by integrating data from ExpressionAtlas and TCGA, revealing B cell-related NFkappaB activation differences in cancer.

The paper introduces the OmicsDI REST interface for programmatic access to omics datasets and provides R and Python libraries for interacting with the API.

The paper describes Wikidata as a FAIR knowledge graph for life sciences, showcasing its breadth, tools for integration, and use cases in biomedical analysis.

Memote, open-source Python software, runs a community-maintained, partly scored test suite (consistency, biomass, annotations, optional experimental data) on SBML-encoded genome-scale metabolic models, once or through continuous integration.

Phoenix Enhancer improves proteomics data analysis by clustering spectra to identify potential mis-identifications and enhance peptide spectrum match confidence.

The paper curates a dataset of physical molecular interactions between SARS-CoV-2, SARS-CoV-1, and other Coronaviridae proteins, providing a resource for studying virus-host interactions.

This paper explores how to quantify the impact of public omics data by examining usage patterns and the role of FAIR principles in data discovery and reuse.

PathwayMatcher maps omics data to pathway networks considering protein isoforms and modifications, enabling more precise pathway analysis.